48 citations · 92 across the 15 of their papers we have counts for
9 papers · 1 filter
Modular Gaussian Processes for Transfer Learning
Pablo Moreno-Muñoz, Antonio Artés-Rodríguez, Mauricio A. Álvarez
We present a framework for transfer learning based on modular variational Gaussian processes (GP). We develop a module-based method that having a dictionary of well fitted GPs, one…
Deep Autoregressive Models with Spectral Attention
Fernando Moreno-Pino, Pablo M. Olmos, Antonio Artés-Rodríguez
Time series forecasting is an important problem across many domains, playing a crucial role in multiple real-world applications. In this paper, we propose a forecasting architectur…
Recyclable Gaussian Processes
Pablo Moreno-Muñoz, Antonio Artés-Rodríguez, Mauricio A. Álvarez
We present a new framework for recycling independent variational approximations to Gaussian processes. The main contribution is the construction of variational ensembles given a di…
Multinomial Sampling for Hierarchical Change-Point Detection
Lorena Romero-Medrano, Pablo Moreno-Muñoz, Antonio Artés-Rodríguez
Bayesian change-point detection, together with latent variable models, allows to perform segmentation over high-dimensional time-series. We assume that change-points lie on a lower…
Continual Learning for Infinite Hierarchical Change-Point Detection
Pablo Moreno-Muñoz, David Ramírez, Antonio Artés-Rodríguez
Change-point detection (CPD) aims to locate abrupt transitions in the generative model of a sequence of observations. When Bayesian methods are considered, the standard practice is…
Change-Point Detection on Hierarchical Circadian Models
Pablo Moreno-Muñoz, David Ramírez, Antonio Artés-Rodríguez
This paper addresses the problem of change-point detection on sequences of high-dimensional and heterogeneous observations, which also possess a periodic temporal structure. Due to…
Heterogeneous Multi-output Gaussian Process Prediction
Pablo Moreno-Muñoz, Antonio Artés-Rodríguez, Mauricio A. Álvarez
We present a novel extension of multi-output Gaussian processes for handling heterogeneous outputs. We assume that each output has its own likelihood function and use a vector-valu…
Bayesian Nonparametric Crowdsourcing
Pablo G. Moreno, Yee Whye Teh, Fernando Perez-Cruz +1
Crowdsourcing has been proven to be an effective and efficient tool to annotate large datasets. User annotations are often noisy, so methods to combine the annotations to produce r…
Blind Analysis of EGM Signals: Sparsity-Aware Formulation
David Luengo, Javier Via, Sandra Monzon +2
This technical note considers the problems of blind sparse learning and inference of electrogram (EGM) signals under atrial fibrillation (AF) conditions. First of all we introduce…